Overview

Dataset statistics

Number of variables14
Number of observations337096
Missing cells54659
Missing cells (%)1.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory36.0 MiB
Average record size in memory112.0 B

Variable types

Categorical3
DateTime1
Numeric9
Text1

Alerts

VERSIE has constant value ""Constant
DATUM_BESTAND has constant value ""Constant
PEILDATUM has constant value ""Constant
BEHANDELEND_SPECIALISME_CD is highly overall correlated with AANTAL_PAT_PER_SPCHigh correlation
AANTAL_PAT_PER_ZPD is highly overall correlated with AANTAL_SUBTRAJECT_PER_ZPDHigh correlation
AANTAL_SUBTRAJECT_PER_ZPD is highly overall correlated with AANTAL_PAT_PER_ZPDHigh correlation
AANTAL_PAT_PER_DIAG is highly overall correlated with AANTAL_SUBTRAJECT_PER_DIAGHigh correlation
AANTAL_SUBTRAJECT_PER_DIAG is highly overall correlated with AANTAL_PAT_PER_DIAGHigh correlation
AANTAL_PAT_PER_SPC is highly overall correlated with BEHANDELEND_SPECIALISME_CD and 1 other fieldsHigh correlation
AANTAL_SUBTRAJECT_PER_SPC is highly overall correlated with AANTAL_PAT_PER_SPCHigh correlation
GEMIDDELDE_VERKOOPPRIJS has 54659 (16.2%) missing valuesMissing
AANTAL_SUBTRAJECT_PER_ZPD is highly skewed (γ1 = 21.31860066)Skewed

Reproduction

Analysis started2023-07-19 18:00:31.435338
Analysis finished2023-07-19 18:00:50.923706
Duration19.49 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

VERSIE
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
1.0
337096 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1011288
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 337096
100.0%

Length

2023-07-19T18:00:51.018210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-19T18:00:51.151541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 337096
100.0%

Most occurring characters

ValueCountFrequency (%)
1 337096
33.3%
. 337096
33.3%
0 337096
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 674192
66.7%
Other Punctuation 337096
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 337096
50.0%
0 337096
50.0%
Other Punctuation
ValueCountFrequency (%)
. 337096
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1011288
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 337096
33.3%
. 337096
33.3%
0 337096
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1011288
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 337096
33.3%
. 337096
33.3%
0 337096
33.3%

DATUM_BESTAND
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
2023-07-07
337096 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters3370960
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-07-07
2nd row2023-07-07
3rd row2023-07-07
4th row2023-07-07
5th row2023-07-07

Common Values

ValueCountFrequency (%)
2023-07-07 337096
100.0%

Length

2023-07-19T18:00:51.285726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-19T18:00:51.409196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-07-07 337096
100.0%

Most occurring characters

ValueCountFrequency (%)
0 1011288
30.0%
2 674192
20.0%
- 674192
20.0%
7 674192
20.0%
3 337096
 
10.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2696768
80.0%
Dash Punctuation 674192
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1011288
37.5%
2 674192
25.0%
7 674192
25.0%
3 337096
 
12.5%
Dash Punctuation
ValueCountFrequency (%)
- 674192
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3370960
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1011288
30.0%
2 674192
20.0%
- 674192
20.0%
7 674192
20.0%
3 337096
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3370960
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1011288
30.0%
2 674192
20.0%
- 674192
20.0%
7 674192
20.0%
3 337096
 
10.0%

PEILDATUM
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
2023-07-01
337096 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters3370960
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-07-01
2nd row2023-07-01
3rd row2023-07-01
4th row2023-07-01
5th row2023-07-01

Common Values

ValueCountFrequency (%)
2023-07-01 337096
100.0%

Length

2023-07-19T18:00:51.542838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-19T18:00:51.666650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-07-01 337096
100.0%

Most occurring characters

ValueCountFrequency (%)
0 1011288
30.0%
2 674192
20.0%
- 674192
20.0%
3 337096
 
10.0%
7 337096
 
10.0%
1 337096
 
10.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2696768
80.0%
Dash Punctuation 674192
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1011288
37.5%
2 674192
25.0%
3 337096
 
12.5%
7 337096
 
12.5%
1 337096
 
12.5%
Dash Punctuation
ValueCountFrequency (%)
- 674192
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3370960
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1011288
30.0%
2 674192
20.0%
- 674192
20.0%
3 337096
 
10.0%
7 337096
 
10.0%
1 337096
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3370960
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1011288
30.0%
2 674192
20.0%
- 674192
20.0%
3 337096
 
10.0%
7 337096
 
10.0%
1 337096
 
10.0%

JAAR
Date

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
Minimum2012-01-01 00:00:00
Maximum2023-01-01 00:00:00
2023-07-19T18:00:51.775267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:00:51.914481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)

BEHANDELEND_SPECIALISME_CD
Real number (ℝ)

HIGH CORRELATION 

Distinct28
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean447.49793
Minimum301
Maximum8418
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2023-07-19T18:00:52.070969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum301
5-th percentile302
Q1305
median313
Q3322
95-th percentile361
Maximum8418
Range8117
Interquartile range (IQR)17

Descriptive statistics

Standard deviation1025.4821
Coefficient of variation (CV)2.2915906
Kurtosis56.322438
Mean447.49793
Median Absolute Deviation (MAD)8
Skewness7.6319158
Sum1.5084976 × 108
Variance1051613.5
MonotonicityNot monotonic
2023-07-19T18:00:52.250585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
305 47282
14.0%
313 43837
13.0%
303 38807
11.5%
330 26683
 
7.9%
316 22847
 
6.8%
308 17998
 
5.3%
306 14149
 
4.2%
324 13803
 
4.1%
301 13551
 
4.0%
304 10997
 
3.3%
Other values (18) 87142
25.9%
ValueCountFrequency (%)
301 13551
 
4.0%
302 7443
 
2.2%
303 38807
11.5%
304 10997
 
3.3%
305 47282
14.0%
306 14149
 
4.2%
307 5946
 
1.8%
308 17998
 
5.3%
310 3715
 
1.1%
313 43837
13.0%
ValueCountFrequency (%)
8418 4614
 
1.4%
8416 866
 
0.3%
1900 223
 
0.1%
390 945
 
0.3%
389 3558
 
1.1%
362 4394
 
1.3%
361 2450
 
0.7%
335 3425
 
1.0%
330 26683
7.9%
329 888
 
0.3%
Distinct1900
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
2023-07-19T18:00:52.714313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.35117
Min length2

Characters and Unicode

Total characters1129666
Distinct characters25
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)< 0.1%

Sample

1st row11
2nd row11
3rd row13
4th row11
5th row12
ValueCountFrequency (%)
101 1445
 
0.4%
402 1393
 
0.4%
301 1361
 
0.4%
403 1359
 
0.4%
201 1281
 
0.4%
203 1266
 
0.4%
401 1141
 
0.3%
404 1123
 
0.3%
409 1096
 
0.3%
302 1086
 
0.3%
Other values (1890) 324545
96.3%
2023-07-19T18:00:53.378826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 216025
19.1%
0 207474
18.4%
2 149690
13.3%
3 122411
10.8%
5 87045
7.7%
9 81402
 
7.2%
4 80155
 
7.1%
7 66523
 
5.9%
6 58885
 
5.2%
8 48622
 
4.3%
Other values (15) 11434
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1118232
99.0%
Uppercase Letter 11434
 
1.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
G 2145
18.8%
M 1923
16.8%
B 1379
12.1%
Z 980
8.6%
E 965
8.4%
D 749
 
6.6%
A 737
 
6.4%
F 721
 
6.3%
K 371
 
3.2%
C 369
 
3.2%
Other values (5) 1095
9.6%
Decimal Number
ValueCountFrequency (%)
1 216025
19.3%
0 207474
18.6%
2 149690
13.4%
3 122411
10.9%
5 87045
7.8%
9 81402
 
7.3%
4 80155
 
7.2%
7 66523
 
5.9%
6 58885
 
5.3%
8 48622
 
4.3%

Most occurring scripts

ValueCountFrequency (%)
Common 1118232
99.0%
Latin 11434
 
1.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
G 2145
18.8%
M 1923
16.8%
B 1379
12.1%
Z 980
8.6%
E 965
8.4%
D 749
 
6.6%
A 737
 
6.4%
F 721
 
6.3%
K 371
 
3.2%
C 369
 
3.2%
Other values (5) 1095
9.6%
Common
ValueCountFrequency (%)
1 216025
19.3%
0 207474
18.6%
2 149690
13.4%
3 122411
10.9%
5 87045
7.8%
9 81402
 
7.3%
4 80155
 
7.2%
7 66523
 
5.9%
6 58885
 
5.3%
8 48622
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1129666
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 216025
19.1%
0 207474
18.4%
2 149690
13.3%
3 122411
10.8%
5 87045
7.7%
9 81402
 
7.2%
4 80155
 
7.1%
7 66523
 
5.9%
6 58885
 
5.2%
8 48622
 
4.3%
Other values (15) 11434
 
1.0%

ZORGPRODUCT_CD
Real number (ℝ)

Distinct6120
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.399477 × 108
Minimum10501002
Maximum9.9841808 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2023-07-19T18:00:53.707225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10501002
5-th percentile28999038
Q199799048
median1.4959902 × 108
Q39.90004 × 108
95-th percentile9.9051605 × 108
Maximum9.9841808 × 108
Range9.8791708 × 108
Interquartile range (IQR)8.9020495 × 108

Descriptive statistics

Standard deviation4.2879411 × 108
Coefficient of variation (CV)0.9746479
Kurtosis-1.7333869
Mean4.399477 × 108
Median Absolute Deviation (MAD)1.1960002 × 108
Skewness0.47167835
Sum1.4830461 × 1014
Variance1.8386439 × 1017
MonotonicityNot monotonic
2023-07-19T18:00:53.909417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
990004009 2488
 
0.7%
990004007 2437
 
0.7%
990003004 2323
 
0.7%
990004006 1932
 
0.6%
990356076 1788
 
0.5%
131999228 1653
 
0.5%
990356073 1644
 
0.5%
131999164 1628
 
0.5%
990003007 1494
 
0.4%
131999194 1448
 
0.4%
Other values (6110) 318261
94.4%
ValueCountFrequency (%)
10501002 9
< 0.1%
10501003 12
< 0.1%
10501004 12
< 0.1%
10501005 12
< 0.1%
10501007 3
 
< 0.1%
10501008 12
< 0.1%
10501010 12
< 0.1%
10501011 3
 
< 0.1%
11101002 10
< 0.1%
11101003 12
< 0.1%
ValueCountFrequency (%)
998418081 165
< 0.1%
998418080 151
< 0.1%
998418079 38
 
< 0.1%
998418077 9
 
< 0.1%
998418076 9
 
< 0.1%
998418075 7
 
< 0.1%
998418074 237
0.1%
998418073 233
0.1%
998418072 9
 
< 0.1%
998418071 9
 
< 0.1%

AANTAL_PAT_PER_ZPD
Real number (ℝ)

HIGH CORRELATION 

Distinct10307
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean513.20126
Minimum1
Maximum165185
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2023-07-19T18:00:54.098569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median13
Q3100
95-th percentile1723
Maximum165185
Range165184
Interquartile range (IQR)97

Descriptive statistics

Standard deviation3203.1046
Coefficient of variation (CV)6.2414201
Kurtosis409.34858
Mean513.20126
Median Absolute Deviation (MAD)12
Skewness16.765663
Sum1.7299809 × 108
Variance10259879
MonotonicityNot monotonic
2023-07-19T18:00:54.290008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 56505
 
16.8%
2 27579
 
8.2%
3 17983
 
5.3%
4 13101
 
3.9%
5 10256
 
3.0%
6 8684
 
2.6%
7 7165
 
2.1%
8 6113
 
1.8%
9 5520
 
1.6%
10 4919
 
1.5%
Other values (10297) 179271
53.2%
ValueCountFrequency (%)
1 56505
16.8%
2 27579
8.2%
3 17983
 
5.3%
4 13101
 
3.9%
5 10256
 
3.0%
6 8684
 
2.6%
7 7165
 
2.1%
8 6113
 
1.8%
9 5520
 
1.6%
10 4919
 
1.5%
ValueCountFrequency (%)
165185 1
< 0.1%
162462 1
< 0.1%
155870 1
< 0.1%
154484 1
< 0.1%
154259 1
< 0.1%
153588 1
< 0.1%
144715 1
< 0.1%
118397 1
< 0.1%
115938 1
< 0.1%
113250 1
< 0.1%

AANTAL_SUBTRAJECT_PER_ZPD
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct11098
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean608.55576
Minimum1
Maximum240002
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2023-07-19T18:00:54.481738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median14
Q3109
95-th percentile1970
Maximum240002
Range240001
Interquartile range (IQR)106

Descriptive statistics

Standard deviation4132.0186
Coefficient of variation (CV)6.7898768
Kurtosis719.67091
Mean608.55576
Median Absolute Deviation (MAD)13
Skewness21.318601
Sum2.0514171 × 108
Variance17073578
MonotonicityNot monotonic
2023-07-19T18:00:54.676066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 54434
 
16.1%
2 27098
 
8.0%
3 17817
 
5.3%
4 12905
 
3.8%
5 10168
 
3.0%
6 8632
 
2.6%
7 7132
 
2.1%
8 6049
 
1.8%
9 5426
 
1.6%
10 4960
 
1.5%
Other values (11088) 182475
54.1%
ValueCountFrequency (%)
1 54434
16.1%
2 27098
8.0%
3 17817
 
5.3%
4 12905
 
3.8%
5 10168
 
3.0%
6 8632
 
2.6%
7 7132
 
2.1%
8 6049
 
1.8%
9 5426
 
1.6%
10 4960
 
1.5%
ValueCountFrequency (%)
240002 1
< 0.1%
232423 1
< 0.1%
231954 1
< 0.1%
230966 1
< 0.1%
227936 1
< 0.1%
227409 1
< 0.1%
226304 1
< 0.1%
223891 1
< 0.1%
218673 1
< 0.1%
215133 1
< 0.1%

AANTAL_PAT_PER_DIAG
Real number (ℝ)

HIGH CORRELATION 

Distinct9186
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7695.9706
Minimum1
Maximum230642
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2023-07-19T18:00:54.868171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile35
Q1372
median1664
Q36235
95-th percentile37050
Maximum230642
Range230641
Interquartile range (IQR)5863

Descriptive statistics

Standard deviation18039.18
Coefficient of variation (CV)2.3439772
Kurtosis34.342309
Mean7695.9706
Median Absolute Deviation (MAD)1532
Skewness5.0782342
Sum2.5942809 × 109
Variance3.25412 × 108
MonotonicityNot monotonic
2023-07-19T18:00:55.055404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21 592
 
0.2%
9 588
 
0.2%
8 583
 
0.2%
2 582
 
0.2%
12 570
 
0.2%
3 560
 
0.2%
17 557
 
0.2%
25 551
 
0.2%
19 549
 
0.2%
7 534
 
0.2%
Other values (9176) 331430
98.3%
ValueCountFrequency (%)
1 506
0.2%
2 582
0.2%
3 560
0.2%
4 522
0.2%
5 528
0.2%
6 510
0.2%
7 534
0.2%
8 583
0.2%
9 588
0.2%
10 471
0.1%
ValueCountFrequency (%)
230642 23
< 0.1%
228000 23
< 0.1%
224865 19
< 0.1%
218430 24
< 0.1%
214509 17
< 0.1%
213516 25
< 0.1%
211593 17
< 0.1%
210417 19
< 0.1%
205347 17
< 0.1%
200603 16
< 0.1%

AANTAL_SUBTRAJECT_PER_DIAG
Real number (ℝ)

HIGH CORRELATION 

Distinct10287
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11174.117
Minimum1
Maximum370143
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2023-07-19T18:00:55.236460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile44
Q1493
median2329
Q39079
95-th percentile52658
Maximum370143
Range370142
Interquartile range (IQR)8586

Descriptive statistics

Standard deviation27047.909
Coefficient of variation (CV)2.4205857
Kurtosis37.581068
Mean11174.117
Median Absolute Deviation (MAD)2160
Skewness5.3055838
Sum3.7667502 × 109
Variance7.3158936 × 108
MonotonicityNot monotonic
2023-07-19T18:00:55.430370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 493
 
0.1%
2 474
 
0.1%
17 450
 
0.1%
23 450
 
0.1%
8 450
 
0.1%
4 448
 
0.1%
10 447
 
0.1%
12 446
 
0.1%
6 445
 
0.1%
5 444
 
0.1%
Other values (10277) 332549
98.7%
ValueCountFrequency (%)
1 421
0.1%
2 474
0.1%
3 493
0.1%
4 448
0.1%
5 444
0.1%
6 445
0.1%
7 433
0.1%
8 450
0.1%
9 363
0.1%
10 447
0.1%
ValueCountFrequency (%)
370143 23
< 0.1%
365356 23
< 0.1%
348485 25
< 0.1%
344335 24
< 0.1%
341653 19
< 0.1%
339129 19
< 0.1%
323756 20
< 0.1%
315779 17
< 0.1%
310778 17
< 0.1%
298646 17
< 0.1%

AANTAL_PAT_PER_SPC
Real number (ℝ)

HIGH CORRELATION 

Distinct324
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean669598.78
Minimum927
Maximum1487636
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2023-07-19T18:00:55.630044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum927
5-th percentile33050
Q1254307
median762394
Q31026335
95-th percentile1332343
Maximum1487636
Range1486709
Interquartile range (IQR)772028

Descriptive statistics

Standard deviation424421.32
Coefficient of variation (CV)0.63384424
Kurtosis-1.1709504
Mean669598.78
Median Absolute Deviation (MAD)317739
Skewness-0.054794597
Sum2.2571907 × 1011
Variance1.8013346 × 1011
MonotonicityNot monotonic
2023-07-19T18:00:55.830008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
880930 5102
 
1.5%
874095 4354
 
1.3%
843975 4347
 
1.3%
894314 4333
 
1.3%
880475 4273
 
1.3%
897707 4212
 
1.2%
765020 4089
 
1.2%
803496 4028
 
1.2%
762394 3949
 
1.2%
1080827 3890
 
1.2%
Other values (314) 294519
87.4%
ValueCountFrequency (%)
927 52
 
< 0.1%
1030 168
 
< 0.1%
1344 56
 
< 0.1%
1440 77
 
< 0.1%
1456 438
0.1%
1610 130
 
< 0.1%
1829 138
 
< 0.1%
1920 131
 
< 0.1%
2495 173
 
0.1%
2520 190
0.1%
ValueCountFrequency (%)
1487636 2975
0.9%
1450398 3048
0.9%
1421714 3564
1.1%
1344312 3543
1.1%
1340618 3441
1.0%
1332343 3545
1.1%
1316405 3463
1.0%
1282943 3576
1.1%
1267128 3351
1.0%
1265244 1177
 
0.3%

AANTAL_SUBTRAJECT_PER_SPC
Real number (ℝ)

HIGH CORRELATION 

Distinct325
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1088791.5
Minimum946
Maximum2664625
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2023-07-19T18:00:56.030782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum946
5-th percentile39816
Q1368644
median1106947
Q31772014
95-th percentile2548457
Maximum2664625
Range2663679
Interquartile range (IQR)1403370

Descriptive statistics

Standard deviation758468.58
Coefficient of variation (CV)0.69661512
Kurtosis-0.8588756
Mean1088791.5
Median Absolute Deviation (MAD)697808
Skewness0.30856052
Sum3.6702724 × 1011
Variance5.7527459 × 1011
MonotonicityNot monotonic
2023-07-19T18:00:56.233001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1211794 5102
 
1.5%
1281489 4354
 
1.3%
1216251 4347
 
1.3%
1315573 4333
 
1.3%
1300437 4273
 
1.3%
1341835 4212
 
1.2%
1155938 4089
 
1.2%
1205345 4028
 
1.2%
1109997 3949
 
1.2%
2548457 3890
 
1.2%
Other values (315) 294519
87.4%
ValueCountFrequency (%)
946 52
 
< 0.1%
1031 168
< 0.1%
1378 56
 
< 0.1%
1440 77
 
< 0.1%
1468 255
0.1%
1800 183
0.1%
1861 130
< 0.1%
2097 138
< 0.1%
2195 131
< 0.1%
2534 12
 
< 0.1%
ValueCountFrequency (%)
2664625 3866
1.1%
2663221 3793
1.1%
2619060 3789
1.1%
2593887 3844
1.1%
2548457 3890
1.2%
2480210 3851
1.1%
2401042 3846
1.1%
2178680 3757
1.1%
2062274 3811
1.1%
2052299 1168
 
0.3%

GEMIDDELDE_VERKOOPPRIJS
Real number (ℝ)

MISSING 

Distinct3590
Distinct (%)1.3%
Missing54659
Missing (%)16.2%
Infinite0
Infinite (%)0.0%
Mean3578.4834
Minimum70
Maximum287220
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2023-07-19T18:00:56.419251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum70
5-th percentile140
Q1470
median1240
Q34155
95-th percentile13620
Maximum287220
Range287150
Interquartile range (IQR)3685

Descriptive statistics

Standard deviation6536.2725
Coefficient of variation (CV)1.8265482
Kurtosis141.73423
Mean3578.4834
Median Absolute Deviation (MAD)1015
Skewness7.1037935
Sum1.0106961 × 109
Variance42722858
MonotonicityNot monotonic
2023-07-19T18:00:56.605961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
160 2125
 
0.6%
105 1957
 
0.6%
110 1791
 
0.5%
185 1729
 
0.5%
180 1629
 
0.5%
175 1457
 
0.4%
300 1417
 
0.4%
140 1396
 
0.4%
145 1387
 
0.4%
120 1350
 
0.4%
Other values (3580) 266199
79.0%
(Missing) 54659
 
16.2%
ValueCountFrequency (%)
70 226
 
0.1%
75 75
 
< 0.1%
80 362
 
0.1%
85 921
0.3%
90 668
 
0.2%
95 720
 
0.2%
100 1013
0.3%
105 1957
0.6%
110 1791
0.5%
115 1109
0.3%
ValueCountFrequency (%)
287220 8
< 0.1%
148910 3
 
< 0.1%
142835 4
< 0.1%
122155 4
< 0.1%
116765 3
 
< 0.1%
109725 7
< 0.1%
108570 7
< 0.1%
107655 4
< 0.1%
101270 8
< 0.1%
99525 5
< 0.1%

Interactions

2023-07-19T18:00:48.278185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:00:36.477419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:00:38.046813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:00:39.658903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:00:41.064617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:00:42.457350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:00:43.830799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:00:45.290599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:00:46.846531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:00:48.450964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:00:36.648173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:00:38.254305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:00:39.824962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:00:41.229258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:00:42.618758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:00:44.002410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:00:45.464409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:00:47.013125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:00:48.608581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:00:36.805827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:00:38.434118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:00:39.977848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:00:41.381633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:00:42.768817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:00:44.165187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:00:45.619372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:00:47.165214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:00:48.767394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:00:36.972744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:00:38.624349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:00:40.130266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:00:41.535038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:00:42.919542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:00:44.328644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:00:45.781326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:00:47.321705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:00:48.922752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:00:37.141315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:00:38.784764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:00:40.281874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:00:41.681696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:00:43.066845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:00:44.486497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:00:45.938635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:00:47.472527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:00:49.069389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:00:37.293637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:00:38.932743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:00:40.427367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:00:41.827147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:00:43.206296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:00:44.635583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:00:46.197137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:00:47.619564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:00:49.234432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:00:37.461580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:00:39.094543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:00:40.589434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:00:41.985942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:00:43.362561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:00:44.799748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:00:46.362744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:00:47.781786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:00:49.400246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:00:37.629463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:00:39.256966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:00:40.750981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:00:42.148687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:00:43.524756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:00:44.967846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:00:46.526796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:00:47.951298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:00:49.555494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:00:37.799318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:00:39.504152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:00:40.909329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:00:42.301790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:00:43.677206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:00:45.129375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:00:46.685576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-07-19T18:00:48.107748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-07-19T18:00:56.741045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
BEHANDELEND_SPECIALISME_CDZORGPRODUCT_CDAANTAL_PAT_PER_ZPDAANTAL_SUBTRAJECT_PER_ZPDAANTAL_PAT_PER_DIAGAANTAL_SUBTRAJECT_PER_DIAGAANTAL_PAT_PER_SPCAANTAL_SUBTRAJECT_PER_SPCGEMIDDELDE_VERKOOPPRIJS
BEHANDELEND_SPECIALISME_CD1.0000.2160.0070.012-0.060-0.054-0.534-0.4540.053
ZORGPRODUCT_CD0.2161.000-0.137-0.145-0.169-0.200-0.356-0.3850.029
AANTAL_PAT_PER_ZPD0.007-0.1371.0000.9960.3340.3320.0940.102-0.295
AANTAL_SUBTRAJECT_PER_ZPD0.012-0.1450.9961.0000.3310.3330.0970.109-0.297
AANTAL_PAT_PER_DIAG-0.060-0.1690.3340.3311.0000.9880.3590.3410.037
AANTAL_SUBTRAJECT_PER_DIAG-0.054-0.2000.3320.3330.9881.0000.3740.3710.046
AANTAL_PAT_PER_SPC-0.534-0.3560.0940.0970.3590.3741.0000.9620.004
AANTAL_SUBTRAJECT_PER_SPC-0.454-0.3850.1020.1090.3410.3710.9621.0000.001
GEMIDDELDE_VERKOOPPRIJS0.0530.029-0.295-0.2970.0370.0460.0040.0011.000

Missing values

2023-07-19T18:00:49.786860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-07-19T18:00:50.277207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

VERSIEDATUM_BESTANDPEILDATUMJAARBEHANDELEND_SPECIALISME_CDTYPERENDE_DIAGNOSE_CDZORGPRODUCT_CDAANTAL_PAT_PER_ZPDAANTAL_SUBTRAJECT_PER_ZPDAANTAL_PAT_PER_DIAGAANTAL_SUBTRAJECT_PER_DIAGAANTAL_PAT_PER_SPCAANTAL_SUBTRAJECT_PER_SPCGEMIDDELDE_VERKOOPPRIJS
01.02023-07-072023-07-012019-01-011900119919000236061619158034179304103932NaN
11.02023-07-072023-07-012019-01-01190011991900020111011736191580341793041039322260.0
21.02023-07-072023-07-012019-01-011900139919000262372173079304103932115.0
31.02023-07-072023-07-012019-01-0119001199190000322619158034179304103932475.0
41.02023-07-072023-07-012019-01-0119001299190000825812655177532286179304103932460.0
51.02023-07-072023-07-012019-01-011900139919000222472173079304103932825.0
61.02023-07-072023-07-012019-01-0119001299190001836494042177532286179304103932440.0
71.02023-07-072023-07-012019-01-0119001199190002438014066619158034179304103932305.0
81.02023-07-072023-07-012019-01-0119001299190001610337117471775322861793041039321150.0
91.02023-07-072023-07-012019-01-01190013991900025202172173079304103932290.0
VERSIEDATUM_BESTANDPEILDATUMJAARBEHANDELEND_SPECIALISME_CDTYPERENDE_DIAGNOSE_CDZORGPRODUCT_CDAANTAL_PAT_PER_ZPDAANTAL_SUBTRAJECT_PER_ZPDAANTAL_PAT_PER_DIAGAANTAL_SUBTRAJECT_PER_DIAGAANTAL_PAT_PER_SPCAANTAL_SUBTRAJECT_PER_SPCGEMIDDELDE_VERKOOPPRIJS
3370861.02023-07-072023-07-012016-01-01307M1620108179116243113997013561185662NaN
3370871.02023-07-072023-07-012020-01-01313077979002344113392404210471272619060NaN
3370881.02023-07-072023-07-012014-01-0130324119929905811645731142171418456194700.0
3370891.02023-07-072023-07-012014-01-013031431319992512281107142171418456191845.0
3370901.02023-07-072023-07-012014-01-01303316140801003331047117614217141845619775.0
3370911.02023-07-072023-07-012020-01-0131395411080100711233375104712726190608305.0
3370921.02023-07-072023-07-012014-01-0130314013199925111300331142171418456191845.0
3370931.02023-07-072023-07-012015-01-013623239900620191159866870900815972310.0
3370941.02023-07-072023-07-012015-01-01362223990062019111986207270900815972310.0
3370951.02023-07-072023-07-012014-01-013032161992990591157416197142171418456192095.0